Lesson 4 Slides-Classical and Advanced Techniques for Optimization
... Stochastic programming: studies the case in which some of the constraints depend on random variables. Dynamic programming: studies the case in which the optimization strategy is based on splitting the problem into smaller sub-problems. Combinatorial optimization: is concerned with problems where the ...
... Stochastic programming: studies the case in which some of the constraints depend on random variables. Dynamic programming: studies the case in which the optimization strategy is based on splitting the problem into smaller sub-problems. Combinatorial optimization: is concerned with problems where the ...
Bootstrap Planner: an Iterative Approach to Learn Heuristic
... example, they learn a policy from solving blocksworld problems with n blocks and use it to solve blocksworld problems with (n + 1) blocks. This algorithms is shown to create very effective policies for a variety of planning domains. Here, we aim at learning effective heuristic functions to solve pla ...
... example, they learn a policy from solving blocksworld problems with n blocks and use it to solve blocksworld problems with (n + 1) blocks. This algorithms is shown to create very effective policies for a variety of planning domains. Here, we aim at learning effective heuristic functions to solve pla ...
Lecture Slides (PowerPoint)
... Random-restart Hill-Climbing • Series of HC searches from randomly generated initial states until goal is found • Trivially complete • E[# restarts]=1/p where p is probability of a successful HC given a random initial state • For 8-queens instances with no sideways moves, p≈0.14, so it takes ≈7 ite ...
... Random-restart Hill-Climbing • Series of HC searches from randomly generated initial states until goal is found • Trivially complete • E[# restarts]=1/p where p is probability of a successful HC given a random initial state • For 8-queens instances with no sideways moves, p≈0.14, so it takes ≈7 ite ...
Planning and acting in partially observable stochastic domains
... In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. Problems like the one described above can be modeled as partially observable Markov decision processes (POMDPs). Of course, we are not interested ...
... In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. Problems like the one described above can be modeled as partially observable Markov decision processes (POMDPs). Of course, we are not interested ...
Approaches to Artificial Intelligence
... amounts of computation, most often in the form of a search through a problem space. For example, chess machines are very high performance AI systems, relative to humans, and they ...
... amounts of computation, most often in the form of a search through a problem space. For example, chess machines are very high performance AI systems, relative to humans, and they ...
Using Rewards for Belief State Updates in Partially Observable
... One of the most promising approaches for finding approximate POMDP value functions are point-based methods. In this case, instead of optimizing the value function over the entire belief space, only specific beliefs are considered. In our experiments we used the PBVI algorithm [9] together with regul ...
... One of the most promising approaches for finding approximate POMDP value functions are point-based methods. In this case, instead of optimizing the value function over the entire belief space, only specific beliefs are considered. In our experiments we used the PBVI algorithm [9] together with regul ...
More intriguing parameters of reinforcement
... – the response providing the most immediate reinforcement will increase in rate ...
... – the response providing the most immediate reinforcement will increase in rate ...
Learning to Plan in Complex Stochastic Domains
... Probabilistic planning problems may be formulated as a stochastic sequential decision making problem, modeled as a Markov Decision Process (MDP). In these problems, an agent must find a mapping from states to actions for some subset of the state space that enables the agent to maximize reward over t ...
... Probabilistic planning problems may be formulated as a stochastic sequential decision making problem, modeled as a Markov Decision Process (MDP). In these problems, an agent must find a mapping from states to actions for some subset of the state space that enables the agent to maximize reward over t ...
Improved Memory-Bounded Dynamic Programming for
... state during execution time, it can be used to evaluate the bottom-up policy trees computed by the DP algorithm. A set of belief states can be computed using multiple top-down heuristics – efficient algorithms that find useful top-down policies. Once a top-down heuristic policy is generated, the mos ...
... state during execution time, it can be used to evaluate the bottom-up policy trees computed by the DP algorithm. A set of belief states can be computed using multiple top-down heuristics – efficient algorithms that find useful top-down policies. Once a top-down heuristic policy is generated, the mos ...
lift - Hong Kong University of Science and Technology
... constraints on actions Relations to be learned Whether a relation should be in precondition of A, or effect of A, or not Constraints on relations can be integrated into a global ...
... constraints on actions Relations to be learned Whether a relation should be in precondition of A, or effect of A, or not Constraints on relations can be integrated into a global ...
An Investigation of Selection Hyper
... change characteristics. The simplest approach is to restart the search algorithm each time a change occurs. However, usually the change in the environment is not too drastic and information gained during the previous environments can be used to locate the new optima much quicker. The main problem in ...
... change characteristics. The simplest approach is to restart the search algorithm each time a change occurs. However, usually the change in the environment is not too drastic and information gained during the previous environments can be used to locate the new optima much quicker. The main problem in ...
Learning Long-term Planning in Basketball Using
... Figure 3: Rollouts generated by the HPN and baseline (columns a, b, c). Attention model (column d). Macro-goals (column e). Rollouts. Each frame shows an o↵ensive player (dark green), a rollout (blue) track that extrapolates after 20 frames, the o↵ensive team (light green) and defenders (red). Note ...
... Figure 3: Rollouts generated by the HPN and baseline (columns a, b, c). Attention model (column d). Macro-goals (column e). Rollouts. Each frame shows an o↵ensive player (dark green), a rollout (blue) track that extrapolates after 20 frames, the o↵ensive team (light green) and defenders (red). Note ...
Finite-time Analysis of the Multiarmed Bandit Problem*
... that 0 < d < 1). Note also that this is a result stronger than those of Theorems 1 and 2, as it establishes a bound on the instantaneous regret. However, unlike Theorems 1 and 2, here we need to know a lower bound d on the difference between the reward expectations of the best and the second best ma ...
... that 0 < d < 1). Note also that this is a result stronger than those of Theorems 1 and 2, as it establishes a bound on the instantaneous regret. However, unlike Theorems 1 and 2, here we need to know a lower bound d on the difference between the reward expectations of the best and the second best ma ...
Learning Algorithms for Separable Approximations of
... denote the vectors (x1 , . . . , xn ), (v1 , . . . , vn ) and (v1+ , . . . , vn+ ). In order to compute any vik k , we only need to know if xki ≤ Dik . For the newsvendor problem, this translates into or vi+ knowing whether the newsvendor has sold all the newspapers or not, rather than observing the ...
... denote the vectors (x1 , . . . , xn ), (v1 , . . . , vn ) and (v1+ , . . . , vn+ ). In order to compute any vik k , we only need to know if xki ≤ Dik . For the newsvendor problem, this translates into or vi+ knowing whether the newsvendor has sold all the newspapers or not, rather than observing the ...
Automated Bidding Strategy Adaption using Learning Agents in
... In the previous section we have outlined the idea of reinforcement learning and -learning as a specific reinforcement learning method. In this section we present the results of a comparison of two different -functions implemented for consumer-agents acting in an electricity market. The consumer agen ...
... In the previous section we have outlined the idea of reinforcement learning and -learning as a specific reinforcement learning method. In this section we present the results of a comparison of two different -functions implemented for consumer-agents acting in an electricity market. The consumer agen ...
Chapter 4 Methods
... nPrintln(m, i); //where m is String and i is int message and n are parameters m and i are arguments ...
... nPrintln(m, i); //where m is String and i is int message and n are parameters m and i are arguments ...
Why Machine Learning? - Lehrstuhl für Informatik 2
... Artificial Intelligence:Learning: Learning symbolic representation of concepts, ML as search problem , Prior knowledge + training examples guide the learning-process Bayesian Methods:Calculating probabilities of the hypotheses, Bayesian-classifier Theory of the computational complexity: Theoretical ...
... Artificial Intelligence:Learning: Learning symbolic representation of concepts, ML as search problem , Prior knowledge + training examples guide the learning-process Bayesian Methods:Calculating probabilities of the hypotheses, Bayesian-classifier Theory of the computational complexity: Theoretical ...
error backpropagation algorithm
... as incremental approach. When the weights are changed only after all the training patterns are presented then it is called as batch mode. This mode requires additional local storage for each connection to maintain the immediate weight changes. The BP learning algorithm is an example of optimization ...
... as incremental approach. When the weights are changed only after all the training patterns are presented then it is called as batch mode. This mode requires additional local storage for each connection to maintain the immediate weight changes. The BP learning algorithm is an example of optimization ...
Ensemble Learning
... learners. Then, the base learners are combined to use, where among the most popular combination schemes are majority voting for classification and weighted averaging for regression. Generally, to get a good ensemble, the base learners should be as more accurate as possible, and as more diverse as po ...
... learners. Then, the base learners are combined to use, where among the most popular combination schemes are majority voting for classification and weighted averaging for regression. Generally, to get a good ensemble, the base learners should be as more accurate as possible, and as more diverse as po ...
Reinforcement Learning and Automated Planning
... • The solution to such a problem is a sequence of actions, which if applied to I leads to a state S’ such as S’ ⊇ G. Usually, in the description of domains, action schemas (also called operators) are used instead of actions. Action schemas contain variables that can be instantiated using the availab ...
... • The solution to such a problem is a sequence of actions, which if applied to I leads to a state S’ such as S’ ⊇ G. Usually, in the description of domains, action schemas (also called operators) are used instead of actions. Action schemas contain variables that can be instantiated using the availab ...